Medical imaging is increasingly being used for identification of medical problems for patients. Image registration is a key component of post-processing medical images. Image registration typically consists of finding a geometric transformation that aligns a pair of images or volumes. The geometric transformation can be used to compensate for motion, detect changes, or fuse complementary information provided by different acquisition devices. Proposed conventional methods for such transformations range from feature based techniques, that establish correspondences between geometric features extracted from images, to intensity-based approaches. In the case of intensity-based approaches, image registration is often formulated as an optimization problem, where best alignment is obtained by maximizing a measure of intensity similarity between homologous points of a reference image and a study image. In particular, statistical and information theoretic similarity measures have proven very effective in practice and can even solve multimodal registration problems.
The needs of patients varies from general investigative procedures to more evaluative procedures where precision is required. Thus, in some circumstances, great precision is required, for example, in vasculature investigation in the brain.
A potential application of such techniques is the alignment of brain image scans in Magnetic Resonance Image (MRI) or computer tomography (CT). For instance, these scans allow a three dimensional representation of the patients brain to enable the physician to more accurately guide the instrument in neurosurgery.
Currently, however, the conventional methods and apparatus used to attempt to align two images, for example using statistical methods based on Kullback-Leibler (KL) divergence, Mutual Information (MI) and Normalized Mutual Information (NMI) may not provide results that are accurate and consequently information that is available to medical professionals can be misleading. As patients may be evaluated with different types of scanning equipment, data scans from one machine do not necessarily correlate to data scans of other machines. The machines commonly used can differ in configuration so there is a need to provide an arrangement and method to align the data scans of the different machines in a timely and cost efficient manner.
There is a need, therefore, to provide a method and apparatus to allow two images or two data sets of images to be aligned in a highly accurate manner, such that continuity of data is attained.
There is also a need to provide a method and apparatus that is computationally compact and efficient.
There is also a need for a method and apparatus that will aid physicians and technicians in comparison of data values between data scan sets for translation, rotation, scaling and arbitrary non-rigid transformation.
There is a still further need to provide a method and apparatus that allows two images or two data sets of images from differing machines to be compared in a highly accurate manner such that continuity of data is attained, other than standardized evaluative methods, using Kullback-Leibler evaluative techniques.
There is a still further need to provide a method and apparatus to allow any set of images, other than those derived from medical imaging devices, to be compared to one another.